Intelligent scheduling control method for orderly charging guide rail type robot for new energy automobile

Abstract

The invention discloses an intelligent scheduling control method for a new energy automobile ordered charging guide rail type robot, and the method comprises the following steps: a straight rail + U-shaped charging rail is provided, the straight rail + U-shaped charging rail is provided with a guide rail type robot, the straight rail + U-shaped charging rail is two straight rails arranged side by side, and the two straight rails arranged side by side are connected through a U shape; two rows of parking spaces are provided and located on the two straight rails respectively, one row of parking spaces is numbered from the minimum parking space to the minimum parking space + m from left to right, and the other row of parking spaces is numbered from the maximum parking space to the maximum parking space-n from left to right; the position of the guide rail type robot is set as the current parking position of the robot, the idle pile position and the target position are set, the target position is the current idle charging position, and the idle pile position is the position where a charging pile capable of being used for charging is located; and the idle piles are transmitted to a target position through the optimal path strength through an intelligent scheduling system, a scene recognition module and a path planning algorithm.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicle charging technology, specifically to an intelligent dispatching control method of a rail-type robot for orderly charging of new energy vehicles, in particular to an intelligent dispatching control technology and implementation method (straight rail + U-shaped rail) of a rail-type robot for new energy vehicle charging stations. Background Art

[0002] With the popularity of new energy vehicles, the demand for charging is growing, and the operational efficiency and charging order of charging stations have become key issues. Traditional fixed charging piles are often occupied by oil trucks and overtime during use, which seriously affects the use of charging piles and causes waste of charging resources. In order to solve these pain points, rail-type robots are introduced into charging stations to achieve automated and orderly shared charging. In the prior art, robot scheduling systems mostly use fixed paths or simple algorithms, lack flexibility and intelligent decision-making capabilities, and cannot effectively respond to complex and changing charging needs. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides an intelligent scheduling control method for a rail-type robot for orderly charging of new energy vehicles. The purpose of the present invention is to propose an intelligent scheduling control technology and implementation method, which improves the operating efficiency and charging order of the charging station by optimizing the robot's operating path and scheduling strategy.

[0004] To achieve the above object, the present invention provides the following technical solutions: The intelligent dispatching control method for a rail-type robot for orderly charging of new energy vehicles comprises the following steps: Step S1: providing a straight rail + a U-shaped charging rail, on which a rail-type robot is arranged, and the straight rail + the U-shaped charging rail is two straight rails arranged side by side, and the two straight rails arranged side by side are connected by a U shape; Step S2: providing two rows of parking spaces, respectively located on two straight tracks, wherein the parking spaces in one row are numbered from the smallest parking space to the smallest parking space+m from left to right, and the parking spaces in the other row are numbered from the largest parking space to the largest parking space-n from left to right, wherein m and n are natural numbers greater than 1; Step S3: setting the position of the rail-type robot as the current docking position of the robot, and setting the idle pile position and the target position, wherein the target position is the current idle charging position, and the idle pile position is the position of the charging pile that can be used for charging; Step S4: The idle piles are transported to the target location via the optimal path through the intelligent scheduling system, scene recognition module, and path planning algorithm.

[0005] As a further solution of the present invention, the intelligent scheduling system is used to monitor the status of the charging station in real time, including the current position of the rail-mounted robot, the number and position of the idle charging piles, and the position of the vehicle to be charged; the scenario recognition module intelligently recognizes different charging scenarios according to the current docking position, target position of the robot and the position of the idle charging piles; the path planning algorithm designs different path planning algorithms for the charging scheduling scenario of straight rail + U-shaped rail, calculates the optimal path to minimize the moving distance and time of the robot.

[0006] As a further solution of the present invention, Scenario 1: When the distance to the target position ≥ the distance to the docking position, and the idle charging pile is between the robot and the target position, then the Scenario 1 path = (idle charging pile position - current docking position) + (target position - idle charging pile position).

[0007] As a further solution of the present invention, Scenario 2: When the distance to the target position ≥ the distance to the docking position, and the idle charging pile is between the robot and the minimum parking space, then the Scenario 2 path = (current docking position - idle charging pile position) + (target position - idle charging pile position).

[0008] As a further solution of the present invention, Scenario 3: When the distance to the target position > the distance to the docking position, and the idle charging pile is between the target position and the maximum parking space, then the Scenario 3 path = (idle charging pile position - current docking position) + (idle charging pile position - target position).

[0009] As a further solution of the present invention, Scenario 4: When the distance to the target position < the distance to the docking position, and the idle charging pile is between the robot and the target position, then the Scenario 4 path = (current docking position - idle charging pile position) + (idle charging pile position - target position).

[0010] As a further solution of the present invention, Scenario 5: When the distance to the target position < the distance to the docking position, and the idle charging pile is between the target position and the maximum parking space, then the Scenario 5 path = (idle charging pile position - current docking position) + (idle charging pile position - target position).

[0011] As a further solution of the present invention, Scenario 6: When the distance to the target position < the distance to the docking position, and the idle charging pile is between the target position and the minimum parking space, then the Scenario 6 path = (current docking position - idle charging pile position) + (target position - idle charging pile position).

[0012] As a further solution of the present invention, first determine that the target position ≥ the current docking position, and then determine whether there is an idle pile between the robot and the target parking space in Scenario 1. If there is, select the first idle pile in the opposite direction of the target parking space and calculate the path length. If not, select the first idle pile in the opposite direction of the robot through Scenario 2 and calculate the path length, and select the first idle pile in the positive direction of the target parking space through Scenario 3 and calculate the path length, and select a suitable path according to the above path lengths; if it is determined that the target position < the current docking position, then determine whether there is an idle pile between the robot and the target parking space in Scenario 4. If there is, select the first idle pile in the positive direction of the target parking space and calculate the path length. If not, select the first idle pile in the positive direction of the robot through Scenario 5 and calculate the path length, and select the first idle pile in the opposite direction of the target parking space through Scenario 6 and calculate the path length, and select a suitable path according to the above path lengths.

[0013] As a further solution of the present invention, after the calculation is completed, summarize all effective paths and lock an optimal path, and send the scheduling parameters to the guide rail robot. The parameters are the positions of the idle piles and the target position, and the guide rail robot executes according to the instructions and gives the results to the scheduling system.

[0014] The present invention has the following beneficial effects: The present invention proposes an intelligent scheduling control technology and implementation method, which improves the operation efficiency and charging order of the charging station by optimizing the operation path and scheduling strategy of the robot. Specifically include: high efficiency, significantly improve the operation efficiency of the charging station and reduce the charging waiting time through intelligent scheduling. Flexibility, can adapt to different charging scenarios and requirements, and has good adaptability and flexibility. Intelligent decision-making, use advanced algorithms and decision support systems to achieve the selection of the optimal path and the issuance of scheduling instructions. Automation, without manual intervention, realizes automatic robot scheduling and control.

[0015] To more clearly illustrate the structural features and effects of the present invention, the present invention will be described in detail below in conjunction with the drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figures 1 - 6 It is a schematic diagram of different application scenarios mentioned in the present invention.

[0017] Figure 7 It is a schematic diagram of the intelligent scheduling control method for the guide rail robot used for the orderly charging of new energy vehicles mentioned in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The present invention will be further described below in conjunction with the drawings and relevant knowledge, and will be described clearly and completely. Obviously, the described applications are only a part of the embodiments of the present invention, rather than all the embodiments.

[0019] The intelligent scheduling control method of the rail-mounted robot for the orderly charging of new energy vehicles in the present invention solves the problem that in the prior art, the robot scheduling system mostly adopts a fixed path or a simple algorithm, lacking flexibility and intelligent decision-making ability, and being unable to effectively cope with complex and changeable charging demands.

[0020] Refer to Figures 1 - 7 As shown, the present invention provides an intelligent scheduling control method for the rail-mounted robot for the orderly charging of new energy vehicles, including the following steps: Step S1: Provide a straight rail + U-shaped charging track. A rail-mounted robot is arranged on the straight rail + U-shaped charging track. The straight rail + U-shaped charging track consists of two parallel straight rails, and the two parallel straight rails are connected by a U-shape. By providing the straight rail + U-shaped charging track, a relatively flexible track layout is constructed. It allows the rail-mounted robot to shuttle flexibly between different areas. Compared with a single straight rail, it can cover a larger range and more parking spaces, improving the space utilization rate of the charging facilities and providing a basic physical structure for realizing efficient charging scheduling.

[0021] Step S2: Provide two rows of parking spaces, which are respectively located on the two straight rails. One row of parking spaces is numbered from the smallest parking space to the smallest parking space + m from left to right, and the parking spaces in the other row are numbered from the largest parking space to the largest parking space - n from left to right, where m and n are natural numbers greater than 1. Setting two rows of parking spaces and numbering them provides a clear identification system for subsequent position recognition and path planning. Through this numbering method, the intelligent scheduling system can clearly determine the position information of each parking space, facilitating the accurate positioning and management of the rail-mounted robot and the vehicle, and helping the scene recognition module accurately judge the relative position relationship between the robot and the target parking space; Step S3: Set the position of the rail-mounted robot as the current docking position of the robot, and set the position of the idle pile and the target position, where the target position is the current idle charging position, and the position of the idle pile is the position where the charging pile available for charging is located. Setting the current docking position of the robot, the position of the idle pile and the target position clarifies the key elements in the entire charging task. The intelligent scheduling system can obtain these position information in real time, understand the real-time state of the charging station, and provide data support for subsequent scheduling decisions.

[0022] Step S4: The idle charging piles are transported to the target location through the intelligent scheduling system, the scene recognition module, and the path planning algorithm. The intelligent scheduling system is used to monitor the status of the charging station in real time, including the current position of the rail-mounted robot, the number and position of the idle charging piles, and the position of the vehicles waiting to be charged. The scene recognition module intelligently recognizes different charging scenarios based on the current docking position of the robot, the target position, and the position of the idle charging piles. The path planning algorithm designs different path planning algorithms for the charging scheduling scenario of straight rails + U-shaped rails, calculates the optimal path, and minimizes the moving distance and time of the robot. Intelligent scheduling system: It monitors various status information of the charging station in real time and is equivalent to the "brain" of the entire charging system. By collecting and analyzing this data, the system can make reasonable scheduling decisions according to the actual situation. For example, when multiple vehicles need to be charged simultaneously, it can reasonably arrange the allocation of robots and idle charging piles to improve the overall charging efficiency. Scene recognition module: It conducts scene recognition based on the current docking position of the robot, the target position, and the position of the idle charging piles, and can adopt different scheduling strategies for different situations. For example, when the robot is far from the target position and there are many obstacles or other vehicles in the middle, the scheduling scheme adopted is necessarily different from the situation where the distance is closer and the path is unobstructed. Path planning algorithm: It is designed for the complex charging scheduling scenario of straight rails + U-shaped rails, can calculate the optimal path, minimize the moving distance and time of the robot, reduce the energy consumption and time cost during the movement of the robot, and improve the response speed and efficiency of the charging service.

[0023] The present invention can achieve high efficiency and reasonable allocation of charging resources: The intelligent scheduling system can obtain the usage status of each charging pile in the charging station, the needs of the vehicles waiting to be charged, etc. in real time, and allocate the vehicles to the most suitable charging piles based on this data, avoiding the simultaneous occurrence of idle charging piles and long waiting times for vehicles, improving the utilization rate of charging resources, and thus enhancing the overall operation efficiency. Optimization of charging task sequencing: The charging tasks are intelligently sequenced according to factors such as the battery power of the vehicle and the urgency of the user's needs. For example, for vehicles with extremely low battery power and urgent travel needs, charging is arranged preferentially, reducing the waiting anxiety of users, and at the same time making the service of the charging station more targeted and efficient.

[0024] Flexibility, adapting to different charging scenarios: Whether it is a charging station in different scenarios such as a large public parking lot, a community parking lot, or a highway service area, the intelligent scheduling system can formulate corresponding scheduling strategies according to the site layout, the number and distribution of charging piles, and the vehicle flow. For example, in a public parking lot during peak hours when vehicles are concentrated, a fast charging priority strategy can be adopted; in a community parking lot during night charging, a power distribution balance strategy can be adopted to meet the charging needs in different scenarios.

[0025] Compatible with multiple charging devices: With the development of charging technology, there are various types and power ratings of charging piles in the market. The intelligent scheduling control technology can effectively interface and work in coordination with charging devices of different brands and specifications. According to the vehicle's charging protocol and battery characteristics, it can automatically match the most suitable charging pile to ensure the smooth progress of the charging process.

[0026] Intelligent decision-making and data-driven path planning: By leveraging big data analysis technology, deeply mine data such as past vehicle traffic and charging behaviors at charging stations to predict charging demands in different time periods and regions. Combining map information and the layout of charging stations, plan the optimal operation path for the charging robot so that it can quickly reach the target charging pile and vehicle, reducing time waste on the way.

[0027] Real-time dynamic adjustment strategy: The intelligent scheduling system continuously monitors the real-time operation status of the charging station, such as the fault status of charging piles and the charging demands of newly entered vehicles. Once it detects a deviation between the actual situation and the preset strategy, it can quickly re-evaluate and adjust the scheduling plan through algorithms, issue new instructions, and ensure that the charging station always operates efficiently.

[0028] Automation, robots autonomously execute tasks: Under the control of the intelligent scheduling system, the charging robot can autonomously complete a series of operations such as moving from the parking space to the charging pile, plugging and unplugging the charging gun, and monitoring the charging process without manual intervention. This not only reduces labor costs but also avoids mistakes that may occur in manual operations, improving the accuracy and safety of charging.

[0029] System automatic fault handling: When abnormal situations occur during the charging process, such as charging pile failures and charging interruptions, the system can automatically detect and trigger the corresponding fault handling process.

[0030] Refer to Figure 1 As shown, Scenario 1: When the distance to the target position ≥ the distance to the docking position, and the idle pile is between the robot and the target position, then Scenario 1 path = (idle pile position - current docking position) + (target position - idle pile position).

[0031] Refer to Figure 2 As shown, Scenario 2: When the distance to the target position ≥ the distance to the docking position, and the idle pile is between the robot and the minimum parking space, then Scenario 2 path = (current docking position - idle pile position) + (target position - idle pile position).

[0032] Refer to Figure 3 As shown, Scenario 3: When the distance to the target position > the distance to the docking position, and the idle pile is between the target position and the maximum parking space, then Scenario 3 path = (idle pile position - current docking position) + (idle pile position - target position).

[0033] Refer to Figure 4 As shown, Scenario 4: When the distance to the target position < the distance to the docking position, and there is an idle charging pile between the robot and the target position, then the path of Scenario 4 = (current docking position - idle charging pile position) + (idle charging pile position - target position).

[0034] Refer to Figure 5 As shown, Scenario 5: When the distance to the target position < the distance to the docking position, and there is an idle charging pile between the target position and the maximum parking space, then the path of Scenario 5 = (idle charging pile position - current docking position) + (idle charging pile position - target position).

[0035] Refer to Figure 6 As shown, Scenario 6: When the distance to the target position < the distance to the docking position, and there is an idle charging pile between the target position and the minimum parking space, then the path of Scenario 6 = (current docking position - idle charging pile position) + (target position - idle charging pile position).

[0036] Refer to Figure 7 As shown, the specific working process of the present invention is as follows: First, determine that the target position ≥ the current docking position, and then determine whether there is an idle charging pile between the robot and the target parking space in Scenario 1. If there is, select the first idle charging pile in the opposite direction of the target parking space and calculate the path length. If not, select the first idle charging pile in the opposite direction of the robot through Scenario 2 and calculate the path length, and select the first idle charging pile in the positive direction of the target parking space through Scenario 3 and calculate the path length, and select a suitable path according to the above path lengths; if it is determined that the target position < the current docking position, then determine whether there is an idle charging pile between the robot and the target parking space in Scenario 4. If there is, select the first idle charging pile in the positive direction of the target parking space and calculate the path length. If not, select the first idle charging pile in the positive direction of the robot through Scenario 5 and calculate the path length, and select the first idle charging pile in the opposite direction of the target parking space through Scenario 6 and calculate the path length, and select a suitable path according to the above path lengths. After the calculation is completed, summarize all valid paths and lock an optimal path, and send the scheduling parameters to the rail-mounted robot. The parameters are the position of the idle charging pile and the target position, and the rail-mounted robot executes according to the instruction and gives the result to the scheduling system.

[0037] The intelligent scheduling system of the present invention monitors the status of the charging station in real time, including the current position of the robot, the number and position of idle charging piles, the position of the vehicle to be charged, etc.; a scenario recognition module; according to the current docking position of the robot, the target position, and the position of the idle charging pile, intelligently identify different charging scenarios (Scenarios 1 to 6); a path planning algorithm (straight rail + U-shaped rail), for the 6 charging scheduling scenarios of the straight rail + U-shaped rail, design different path planning algorithms to calculate the optimal path to minimize the moving distance and time of the robot.

[0038] The technical principle of the present invention has been described above in conjunction with specific embodiments, which are only the preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. Any technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. Those skilled in the art can readily conceive of other specific embodiments of the present invention without creative efforts, and these embodiments will fall within the protection scope of the present invention.

Claims

1. An intelligent dispatching control method for a rail-type robot for orderly charging of new energy vehicles, characterized in that: The following steps are involved: Step S1: providing a straight rail + a U-shaped charging rail, on which a rail-type robot is arranged, and the straight rail + the U-shaped charging rail is two straight rails arranged side by side, and the two straight rails arranged side by side are connected by a U shape; Step S2: providing two rows of parking spaces, respectively located on two straight tracks, wherein the parking spaces in one row are numbered from the smallest parking space to the smallest parking space+m from left to right, and the parking spaces in the other row are numbered from the largest parking space to the largest parking space-n from left to right, wherein m and n are natural numbers greater than 1; Step S3: setting the position of the rail-type robot as the current docking position of the robot, and setting the idle pile position and the target position, wherein the target position is the current idle charging position, and the idle pile position is the position of the charging pile that can be used for charging; Step S4: The idle piles are transported to the target location via the optimal path through the intelligent scheduling system, scene recognition module, and path planning algorithm.

2. The intelligent dispatching control method for the rail-type robot for orderly charging of new energy vehicles as claimed in claim 1 is characterized in that: The intelligent dispatching system is used to monitor the status of the charging station in real time, including the current position of the rail-mounted robot, the number and position of the idle piles, and the position of the vehicle to be charged; the scene recognition module intelligently identifies different charging scenes according to the current docking position of the robot, the target position, and the position of the idle piles; Path planning algorithm, for the charging scheduling scenario of straight rail + U-shaped rail, different path planning algorithms are designed to calculate the optimal path to minimize the robot's moving distance and time.

3. The intelligent dispatching control method for the rail-type robot for orderly charging of new energy vehicles as claimed in claim 2 is characterized in that: Scenario 1: When the distance to the target position ≥ the distance to the docking position, and the idle pile is between the robot and the target position, the scenario 1 path = (idle pile position - current docking position) + (target position - idle pile position).

4. The intelligent dispatching control method for the rail-type robot for orderly charging of new energy vehicles as claimed in claim 3 is characterized in that: Scenario 2: When the distance to the target position is ≥ the distance to the parking position, and the free pile is between the robot and the minimum parking space, the scenario 2 path = (current parking position - free pile position) + (target position - free pile position).

5. The intelligent dispatching control method for the rail-type robot for orderly charging of new energy vehicles as claimed in claim 4 is characterized in that: Scenario 3: When the distance to the target position is greater than the distance to the parking position, and the idle pile is between the target position and the maximum parking space, the scenario 3 path = (idle pile position - current parking position) + (idle pile position - target position).

6. The intelligent dispatching control method for the rail-type robot for orderly charging of new energy vehicles as claimed in claim 5 is characterized in that: Scenario 4: When the distance to the target position is less than the distance to the docking position, and the idle pile is between the robot and the target position, the scenario 4 path = (current docking position - idle pile position) + (idle pile position - target position).

7. The intelligent dispatching control method for the rail-type robot for orderly charging of new energy vehicles as claimed in claim 6 is characterized in that: Scenario 5: When the distance to the target position is less than the distance to the parking position, and the free pile is between the target position and the maximum parking space, the scenario 5 path = (free pile position - current parking position) + (free pile position - target position).

8. The intelligent dispatching control method for the rail-type robot for orderly charging of new energy vehicles as claimed in claim 7 is characterized in that: Scenario 6: When the distance to the target position is less than the distance to the parking position, and the free pile is between the target position and the minimum parking space, the scenario 6 path = (current parking position - free pile position) + (target position - free pile position).

9. The intelligent dispatching control method for the rail-type robot for orderly charging of new energy vehicles as claimed in claim 8, characterized in that: First, determine whether the target position is ≥ the current parking position, and then determine whether there is an idle pile between the robot and the target parking space in scene 1. If so, select the first idle pile in the opposite direction of the target parking space and calculate the path length. If not, select the first idle pile in the opposite direction of the robot through scene 2 and calculate the path length, and select the first idle pile in the positive direction of the target parking space through scene 3 and calculate the path length. Select a suitable path based on the above path length; if it is determined that the target position is < the current parking position, then determine whether there is an idle pile between the robot and the target parking space in scene 4. If so, select the first idle pile in the positive direction of the target parking space and calculate the path length. If not, select the first idle pile in the positive direction of the robot through scene 5 and calculate the path length, and select the first idle pile in the opposite direction of the target parking space through scene 6 and calculate the path length. Select a suitable path based on the above path length.

10. The intelligent dispatching control method for the rail-type robot for orderly charging of new energy vehicles as claimed in claim 9, characterized in that: After the calculation is completed, all valid paths are summarized and locked into an optimal path. The scheduling parameters are sent to the rail robot, which are the idle pile position and the target position. The rail robot executes according to the instructions and sends the results to the scheduling system.

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